brain mapping in mice

Characterizing large-scale patterns of neural activity in the mouse brain during complex behavior. By analyzing the structure of population recordings — and targeted perturbations of neural activity — we hope to uncover the rules underlying neural representation and computation.

brain mapping in zebrafish

Whole-brain characterization of sensory-motor computation in zebrafish using light-sheet imaging. We hope to uncover how different circuits and brain areas coordinate sensory processing and behavior.



A library for large-scale image and time series analysis in Python using Spark. We use it to analyze neural data, but it has a wide range of applications, including satellite imagery, sensor data, and video.


A data-visualization server providing API-based access to reproducible, web-based, interactive visualizations. It includes a core set of visualization types, but is built for extendability and customization.


A powerful open-source platform for large-scale analytics. It's at the core of many of our analytic efforts, and we actively contribute to the project, including algorithms for streaming machine learning, and enhancements to its Python API.

open science


We want to minimize the barrier between data and data exploration. Using the tmpnb service and Docker containers, our notebooks provide interactive computing environments for tutorials, data set exploration, and analysis.


Reproducible science requires making our datasets available in formats that are standardized and immediately accessible to computing environments in the cloud. Our web portal provides access to neuroscience data sets hosted on Amazon S3.


To avoid reinventing the wheel in science, we need to all get on the same page. For one key data analysis problem — source extraction from calcium imaging data — we and others have built a community platform to compare and vet algorithms on standardized data sets in reprodicble environments.